The effects of fine-scale surface roughness and grain size on 300 kHz multibeam backscatter intensity in sandy marine sedimentary environments

Ferrini, V. L.; Flood, Roger D.

doi:10.1016/j.margeo.2005.11.010

Cited by 86 publications

(65 citation statements)

References 32 publications

(62 reference statements)

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“…It is the result of an intricate combination of several physical factors: the seawater-seafloor impedance contrast, the interface roughness and the sediment volume heterogeneity, the signal incidence angle on the seafloor and the acoustical signal frequency (Lurton 2010 Ferrini and Flood 2006) and possibly to infer some of its physical characteristics. However, backscatter data are inherently noisy, showing strong amplitude fluctuations due to the very nature of the scattering process (Lurton 2010), and the possible presence of additive external noise: a first processing stage is to reduce this random fluctuating character by appropriate filtering techniques.…”

Section: Mbes Data Processingmentioning

confidence: 99%

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

et al. 2017

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Section: Mbes Data Processingmentioning

confidence: 99%

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

et al. 2017

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“…bioturbation, heterogeneity of sediment, and thickness of the surface layer (e.g. Hughes Clarke et al, 1997;Nitsche et al, 2004;Ferrini & Flood, 2006). One potential factor could be temperature, but this shows little variation within the Tromsøflaket study area.…”

Section: Broad Scalementioning

confidence: 99%

Habitat complexity and bottom fauna composition at different scales on the continental shelf and slope of northern Norway

et al. 2012

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The MAREANO (Marine AREA database for NOrwegian coast and sea areas) mapping programme includes acquisition of multibeam bathymetry and backscatter data together with a comprehensive, integrated biological and geological sampling programme. Equipment used includes underwater video, box corer, grab, epibenthic sled and beam trawl. Habitat maps are produced by combining information on landscapes, landscape elements, sediment types and biological communities. Video observations provide information about the megafauna diversity of large ([1 cm) epifauna and bottom types, whilst bottom samples describe the composition of epifauna, hyperfauna (crustaceans living in the upper part of the sediment and/or swimming just above the substratum) and infauna, and sediment composition. In this study, two biological data sets are used to study fauna response to environmental heterogeneity at two different spatial scales: (1) broad scale, megahabitat (1-10s km), based on information about megafauna taxa observed during video surveys in the Nordland/ Troms area, (2) fine scale, mesohabitat (10s m-1 km), based on information about species composition documented with video records and bottom sampling gear from the bank ''Tromsøflaket''. In general, the highest diversity is found on bottoms with mixed substrates indicating that substratum heterogeneity is very important for the biodiversity at both scales. The number of taxa shows a maximum at depths between 200 and 700 m followed by a gradual decrease down to 2,200 m. At the broad scale, multibeam data provides a variety of terrain variables that indicate environmental variation (e.g. exposure to currents, interpreted substrates). This analysis identifies six fauna groups associated to specific landscape elements. Diversity of megafauna shows a strong correlation with number of bottom types occurring along video transects. It is highest at the shelf break and decreased with depth on the slope in parallel with a decrease in habitat heterogeneity and temperature. At a fine scale, six biotopes are identified based on megafauna composition with habitat characteristics ranging from homogenous muddy bottom, biotope 1, to the most heterogeneous bottom with [20% rocks and several bottom types present in biotope 6. The macrofauna 123Hydrobiologia ( ) 685:191-219 DOI 10.1007 sampled is used for description of the whole benthic community, including diversity, biomass and production, related to these six biotopes. The variation in percentage cover of substrate types and in particular the cover of hard substrates demonstrate to be a good proxy for the benthic community composition (megaand macrofauna) and its diversity.

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“…Figure 5 shows that there are predominantly gradual boundaries between the different types of seafloor, which prevent more distinct point clusters to form. Acoustic backscatter is not only the result of grain size (Goff et al 2000;Richardson et al 2001;Ferrini and Flood 2006;Daniell et al 2015)-bedforms (e.g., ripples) and biogenic structures (e.g., corals) influence the roughness of the seafloor and hence the backscatter as well. Consequently, this study does not define hard boundaries between the RZs.…”

Section: Discussionmentioning

confidence: 99%

“…Samples 43 and 44 (both unimodal) show slightly coarser first modes than their respective mean grain sizes. Backscatter is highly susceptible to larger grain sizes (Goff et al 2000) and to sorting, in particular when the largest grain size approaches the acoustic wave length used (here~0.75 mm; Ferrini and Flood 2006). This could be one reason for the slightly 'rougher' (cf.…”

Section: Classification With Hard and Soft Boundariesmentioning

confidence: 99%

Seafloor monitoring west of Helgoland (German Bight, North Sea) using the acoustic ground discrimination system RoxAnn

et al. 2016

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Marine habitats of shelf seas are in constant dynamic change and therefore need regular assessment particularly in areas of special interest. In this study, the single-beam acoustic ground discrimination system RoxAnn served to assess seafloor hardness and roughness, and combine these parameters into one variable expressed as RGB (red green blue) color code followed by k-means fuzzy cluster analysis (FCA). The data were collected at a monitoring site west of the island of Helgoland (German Bight, SE North Sea) in the course of four surveys between September 2011 and November 2014. The study area has complex characteristics varying from outcropping bedrock to sandy and muddy sectors with mostly gradual transitions. RoxAnn data enabled to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be quite stable overall; sediment import (including fluid mud) was detected only from the NW. Although hard substrates (boulders, bedrock) are clearly identified, the signal can be modified by inclination and biocover. Manually, six RoxAnn zones were identified; for the FCA, only three classes are suggested. The latter classification based on 'hard' boundaries would suffice for stakeholder issues, but the former classification based on 'soft' boundaries is preferred to meet state-of-the-art scientific objectives.

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The effects of fine-scale surface roughness and grain size on 300 kHz multibeam backscatter intensity in sandy marine sedimentary environments

Cited by 86 publications

References 32 publications

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

Habitat complexity and bottom fauna composition at different scales on the continental shelf and slope of northern Norway

Seafloor monitoring west of Helgoland (German Bight, North Sea) using the acoustic ground discrimination system RoxAnn

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